Area B: Precise DCE-MRI Assessment of Brain Tumors

B 区：脑肿瘤的精确 DCE-MRI 评估

• 批准号: 9483217
• 负责人: Krishna S Nayak
• 金额: $ 156.2万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9483217
• 关键词: Address; Adopted; Adult; Affect; Angiogenesis Inhibitors; Area; Biological Markers; Blood - brain barrier anatomy; Blood Plasma Volume; Brain; Brain Neoplasms; Brain imaging; Clinical; Clinical Trials; Contrast Media; Cytotoxic T-Lymphocyte-Associated Protein 4; Data; Diagnostic; Dictionary; Drug Kinetics; Elements; Evaluation; Extracellular Space; Extravasation; Freedom; Glioma; Image; Immunotherapy; Individual; Institution; Kidney Neoplasms; Kinetics; Left; Lesion; Life Expectancy; Liver neoplasms; Magnetic Resonance Imaging; Malignant neoplasm of brain; Mammary Neoplasms; Maps; Measurement; Measures; Metastatic Melanoma; Metastatic malignant neoplasm to brain; Methods; Modeling; Monte Carlo Method; Motion; Multiple Sclerosis; Neoplasm Metastasis; Outcome Measure; Patients; Positioning Attribute; Primary Brain Neoplasms; Prostatic Neoplasms; Recurrence; Recurrent tumor; Reproducibility; Resolution; Role; Sampling; Techniques; Testing; Therapeutic Agents; Therapeutic Clinical Trial; Thinness; Time; Tissues; Tracer; Work; anticancer research; base; bevacizumab; chemotherapy; clinical imaging; contrast enhanced; image reconstruction; imaging biomarker; imaging study; improved; in vivo; innovation; melanoma; nervous system disorder; neurovascular; novel; novel strategies; novel therapeutics; oncology; predicting response; prognostic; reconstruction; response; spatiotemporal; theories; tool; treatment response; tumor; tumor progression

AREA B: PRECISE DCE-MRI OF BRAIN TUMORS PROJECT SUMMARY This project will develop and validate an improved dynamic contrast-enhanced (DCE) MRI technique for assessing brain tumor response to therapy. Rationale: Historically, increases in tumor size or enhancement have signified tumor progression, and decreases in tumor size have signified treatment response. With the advent of novel chemotherapy agents, including immunotherapy, simple changes in size or enhancement are no longer sufficient to make treatment decisions. We believe that improved DCE-MRI methods can provide new and powerful biomarkers to image brain tumors and detect early response to therapy. This will enhance our ability to prolong survival in a higher proportion of brain tumor patients traditionally regarded as the most dire prognostic group (including recurrent high-grade glioma and metastatic melanoma), who are often left out of clinical trials due to very short life expectancy. Innovation: We propose Specially Tailored Acquisition and Reconstruction (STAR) DCE-MRI, in which acquisition and reconstruction are tailored from an estimation- theoretic point of view to create the most accurate and reproducible tracer kinetic (TK) parameter maps, unlike conventional approaches that optimize the quality of intermediate images. We will fully integrate TK models with DCE-MRI acquisition and reconstruction. Our preliminary data shows 36-fold improvement in spatial resolution and coverage compared to current techniques, with no loss of image quality in brain tumor patients; and we expect to only get better. Compared to current state-of-the-art DCE-MRI, this technique will provide three major advances: 1- exquisite (sub-1 mm isotropic) spatial resolution to quantitatively assess narrow tumor margins and small lesions, 2- whole-brain coverage including all lesions and all surrounding tissue thereby simplifying the exam, 3- robust measurement of patient specific arterial inputs which are required for accurate contrast agent kinetic modeling. Approach: We will optimize, technically validate, and clinically evaluate STAR DCE-MRI method that provides improved quantitative parametric brain maps including blood- brain barrier leakage and fractional plasma volume. Specifically, we will: 1- optimize and technically validate STAR DCE-MRI to produce accurate and reproducible TK parameter maps, 2- produce a robust clinical implementation of STAR DCE-MRI, and 3- clinically evaluate STAR DCE-MRI in patients with brain tumors, specifically those with recurrent high-grade glioma treated with an anti-angiogenic agent, and those with brain melanoma metastases treated with an immunotherapy agent. Broader Impact: Improved quantitative multi- parametric DCE-MRI has a potential role in the assessment of all neurologic diseases that have a microvascular component. This technical work, particularly leveraging specific nonlinear temporal models during image reconstruction, is likely to have implications for DCE-MRI of prostate, renal, breast, and liver tumors as well as outside of DCE-MRI. Importantly it addresses a critical unmet need in oncology in providing a robust, reproducible, high spatio-temporal resolution and high spatial coverage biomarker as a potential end point for clinical trials of novel therapeutic agents in cancer research.

B 区：脑肿瘤的精确 DCE-MRI 项目概要 该项目将开发并验证一种改进的动态对比增强 (DCE) MRI 技术，用于 评估脑肿瘤对治疗的反应。理由：从历史上看，肿瘤大小增加或增强 表明肿瘤进展，而肿瘤大小的减小则表明治疗反应。随着 新型化疗药物（包括免疫疗法）的出现，简单的尺寸改变或增强是 不再足以做出治疗决定。我们相信改进的 DCE-MRI 方法可以提供 新的强大的生物标记物可对脑肿瘤进行成像并检测早期治疗反应。这将增强 我们延长较高比例脑肿瘤患者生存期的能力传统上被认为是最重要的 预后不良的群体（包括复发性高级别胶质瘤和转移性黑色素瘤），经常被排除在外 由于预期寿命非常短，因此无法进行临床试验。创新：我们提出专门定制的收购和 重建 (STAR) DCE-MRI，其中采集和重建根据估计进行定制 理论观点创建最准确和可重复的示踪动力学（TK）参数图，与 优化中间图像质量的传统方法。我们将全面整合TK模型 DCE-MRI 采集和重建。我们的初步数据显示空间提升了 36 倍 与当前技术相比，分辨率和覆盖范围不会降低脑肿瘤患者的图像质量； 我们期望只会变得更好。与当前最先进的 DCE-MRI 相比，该技术将提供 三大进步： 1- 精细（亚 1 毫米各向同性）空间分辨率，可定量评估狭窄 肿瘤边缘和小病灶，2-全脑覆盖，包括所有病灶和所有周围组织 从而简化检查，3-对患者特定动脉输入进行稳健测量，这是所需的 准确的造影剂动力学建模。方法：我们将优化、技术验证和临床 评估 STAR DCE-MRI 方法，该方法提供改进的定量参数脑图，包括血液 脑屏障渗漏和血浆体积分数。具体来说，我们将： 1- 优化和技术验证 STAR DCE-MRI 可生成准确且可重复的 TK 参数图，2- 生成稳健的临床数据 STAR DCE-MRI 的实施，以及 3- 在脑肿瘤患者中临床评估 STAR DCE-MRI， 特别是那些接受抗血管生成药物治疗的复发性高级别神经胶质瘤患者，以及患有脑部疾病的患者 用免疫治疗剂治疗黑色素瘤转移。更广泛的影响：改进定量多 参数 DCE-MRI 在评估所有神经系统疾病方面具有潜在作用 微血管成分。这项技术工作，特别是利用特定的非线性时间模型 在图像重建过程中，可能对前列腺、肾脏、乳房和肝脏的 DCE-MRI 产生影响 肿瘤以及 DCE-MRI 之外的情况。重要的是，它解决了肿瘤学中未满足的关键需求： 稳健、可重复、高时空分辨率和高空间覆盖率的生物标志物作为潜在的终点 癌症研究中新型治疗剂的临床试验点。

项目成果

Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects.

(k,t) 采样对数字参考对象中 DCE MRI 示踪剂动力学参数估计的影响。

• 发表时间: 2020
• 影响因子: 3.3
• 作者: Bliesener, Yannick;Lingala, Sajan G;Haldar, Justin P;Nayak, Krishna S
• 通讯作者: Nayak, Krishna S

Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma.

高级别胶质瘤患者毫米分辨率全脑动态对比增强 MRI 的伪重测评估。

• 发表时间: 2021
• 影响因子: 19.7
• 作者: Bliesener, Yannick;Lebel, R Marc;Acharya, Jay;Frayne, Richard;Nayak, Krishna S
• 通讯作者: Nayak, Krishna S

Tracer kinetic models as temporal constraints during brain tumor DCE-MRI reconstruction.

示踪动力学模型作为脑肿瘤 DCE-MRI 重建过程中的时间约束。

• 发表时间: 2020-01
• 影响因子: 3.8
• 作者: Lingala, Sajan Goud;Guo, Yi;Bliesener, Yannick;Zhu, Yinghua;Lebel, R Marc;Law, Meng;Nayak, Krishna S
• 通讯作者: Nayak, Krishna S

Sparse precontrast T1 mapping for high-resolution whole-brain DCE-MRI.

用于高分辨率全脑 DCE-MRI 的稀疏预对比 T1 映射。

• 发表时间: 2021
• 影响因子: 3.3
• 作者: Zhu, Zhibo;Lebel, R Marc;Bliesener, Yannick;Acharya, Jay;Frayne, Richard;Nayak, Krishna S
• 通讯作者: Nayak, Krishna S

Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network.

使用神经网络进行高效 DCE-MRI 参数和不确定性估计。

• 发表时间: 2020-05
• 影响因子: 10.6
• 作者: Bliesener, Yannick;Acharya, Jay;Nayak, Krishna S
• 通讯作者: Nayak, Krishna S